![]() ![]() ![]() A smart handset or computer displays the ultrasound image as well as other ultrasound data. ![]() Ī few research projects have created a method for transmitting real-time ultrasound data via the internet. The dimensionality of medical image databases is rising exponentially, making it difficult to manage file systems because of the rising amount of data stored due to the growth of medical databases, so the handling of medical data has become a top priority for healthcare providers. The advancement of medical technologies such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging generates vast amounts of data on a daily basis, and the data collected from these devices include multiple dimensions and factors. However, medical devices have now evolved to the point where a large amount of data are generated in the healthcare environment daily. IoT is now also having a huge impact on medical costs and clinical outcomes. Remote monitoring of a patient’s health has also shown to lead to a reduction in hospitalizations and re-admissions. Furthermore, patient engagement and satisfaction have also been enhanced, and interactions with doctors have become easier and more efficient. IoT has been recognized as being one of the most significant research topics in the field of medicine, particularly in image processing. With the introduction of real-time analytics in healthcare, we see technology exceeding the limits of what we can currently achieve in this sector. Since the Internet of Things (IoT) has a significant role in a variety of areas, including IoT-based healthcare, intelligent buildings, and smart monitoring, the evolution of smart medical sensors, gadgets, cloud computing, and healthcare technology is attracting major interest from academics and the healthcare business. This method will enrich the telemedicine facilities both for patients and physicians by assisting with overall monitoring of data. We have implemented an authentic and NS3-based simulation environment to monitor real-time ultrasound image data on the IoT server and have analyzed the system performance, which has been impressive. The Raspberry Pi 3 (RSPI3) forwards the patient information along with a unique encrypted image data link from the FTP server to the IoT server. A Raspberry Pi processes HL7 messages received from medical devices like an ultrasound machine (ULSM) and extracts only the image data for transfer to an FTP server. To address this problem, this study proposes a model in which the system displays images as well as patient data on an IoT dashboard. It is more difficult to handle the monitoring of ultrasound, MRI, or CT image data via IoT. Transferring large amounts of data such as images to IoT servers based on machine-to-machine communication is difficult and time consuming over MQTT and MLLP protocols, and since IoT brokers only handle a limited number of bytes of data, such protocols can only transfer patient information and other text data. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. ![]()
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